Multi-national, large-scale and complex manufacturing systems, such as those for automotive manufacturers, often require a significant investment in production capacity, as well as great management efforts in strategic planning. Capacity-related investment decisions are often irreversible or prohibitively expensive and time-consuming to change once they are in place. Furthermore, such companies operate in uncertain business environments, which can significantly influence the optimal decisions and the systems’ performance. Therefore, a strategic question is how to globally and interactively set production resources for such systems so their optimal performance can be achieved under business uncertainty. Conventional optimisation models in this field often suffer from one or more drawbacks, such as deterministic styles, non-inclusive and non-comprehensive decision terms, non-integrated frameworks, non-empirical approaches, small size practices, local/non-global approaches or difficult-to-use methods/presentations. This research develops a new scenario-based multi-stage stochastic optimisation model, which is capable of designing and planning the production capacity for a multi-national complex manufacturing system over a long-term horizon, under demand and sales price uncertainty
